linear-regression

How can I tell R to use a certain level as reference if I use binary explanatory variables in a regression? It's just using some level by default. lm(x ~ y + as.factor(b)) with b {0, 1, 2, 3, 4} . Let's say I want to use 3 instead of the zero that is used by R. See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b in DF by use of the relevel() function: DF <- within(DF, b <- relevel(b, ref = 3)) m2 <- lm(y ~ x +

I ran a regression: CopierDataRegression <- lm(V1~V2, data=CopierData1) and my task was to obtain a 90% confidence interval for the mean response given V2=6 and 90% prediction interval when V2=6 . I used the following code: X6 <- data.frame(V2=6) predict(CopierDataRegression, X6, se.fit=TRUE, interval="confidence", level=0.90) predict(CopierDataRegression, X6, se.fit=TRUE, interval="prediction", level=0.90) and I got (87.3, 91.9) and (74.5, 104.8) which seems to be correct since the PI should be wider. The output for both also included se.fit = 1.39 which was the same. I don't understand what